7 papers · 1 filter
Which Way Did It Move? Diagnosing and Overcoming Directional Motion Blindness in Video-LLMs
Jongseo Lee, Hyuntak Lee, Sunghun Kim +3
Video Large Language Models (Video-LLMs) have made rapid progress on temporal video understanding, yet many fail at a basic perceptual primitive: signed image-plane motion directio…
Disentangled Concepts Speak Louder Than Words: Explainable Video Action Recognition
Jongseo Lee, Wooil Lee, Gyeong-Moon Park +2
Effective explanations of video action recognition models should disentangle how movements unfold over time from the surrounding spatial context. However, existing methods based on…
ESSENTIAL: Episodic and Semantic Memory Integration for Video Class-Incremental Learning
Jongseo Lee, Kyungho Bae, Kyle Min +2
In this work, we tackle the problem of video classincremental learning (VCIL). Many existing VCIL methods mitigate catastrophic forgetting by rehearsal training with a few temporal…
PCBEAR: Pose Concept Bottleneck for Explainable Action Recognition
Jongseo Lee, Wooil Lee, Gyeong-Moon Park +2
Human action recognition (HAR) has achieved impressive results with deep learning models, but their decision-making process remains opaque due to their black-box nature. Ensuring i…
CA^2ST: Cross-Attention in Audio, Space, and Time for Holistic Video Recognition
Jongseo Lee, Joohyun Chang, Dongho Lee +1
We propose Cross-Attention in Audio, Space, and Time (CA^2ST), a transformer-based method for holistic video recognition. Recognizing actions in videos requires both spatial and te…
PCEvE: Part Contribution Evaluation Based Model Explanation for Human Figure Drawing Assessment and Beyond
Jongseo Lee, Geo Ahn, Seong Tae Kim +1
For automatic human figure drawing (HFD) assessment tasks, such as diagnosing autism spectrum disorder (ASD) using HFD images, the clarity and explainability of a model decision ar…